Research

Understanding hydroclimatology and dynamics of water systems

(Left) Spatial distribution of the 26 selected USGS streamflow gauging stations and 61 intermediate-to-large NID dams across the Colorado River Basin (including both Upper and Lower Colorado Basins). / (Right) Simplified diagram of the riverine network with the local and cumulative alterations measured for both the annual (a) and multi-annual frequency bands (b). Dam-induced local regulations are represented with triangles, and cumulative alterations are denoted with circles.
  • Analyzing the spatiotemporal variability of hydroclimatic characteristics across U.S. watersheds, capturing the interactions between catchment attributes, climatic forcings, and hydrological responses.
  • Investigating the complex interdependencies between basin-scale water systems, hydrological processes, and climate drivers.

Related Publication(s):

  1. Hwang, J., & Devineni, N. (2022). An Improved Zhang’s Dynamic Water Balance Model Using Budyko‐Based Snow Representation for Better Streamflow Predictions. Water Resources Research, 58(1), e2021WR030203.
  2. Hwang, J., Kumar, H., Ruhi, A., Sankarasubramanian, A., & Devineni, N. (2021). Quantifying dam‐induced fluctuations in streamflow frequencies across the Colorado River Basin. Water Resources Research, 57(10), e2021WR029753.
  3. Ruhi, A., Hwang, J., Devineni, N., Mukhopadhyay, S., Kumar, H., Comte, L., … & Sankarasubramanian, A. (2022). How does flow alteration propagate across a large, highly regulated basin? Dam attributes, network context, and implications for biodiversity. Earth’s Future, 10(6), e2021EF002490.
  4. Kumar, H., Hwang, J., Devineni, N., & Sankarasubramanian, A. (2022). Dynamic flow alteration index for complex river networks with cascading reservoir systems. Water Resources Research, 58(1), e2021WR030491.

Git Repository for ZDWBM-msnow: https://github.com/JHwangHydro/ZDWBM-msnow

Attributing hydroclimate extremes and projecting their future impacts

  • Systematically attributing hydrological floods to catchment and climate drivers, enhancing the understanding of the generation process of hydroclimate extremes.
  • Investigate large-scale moisture delivery pathways associated with floods, providing novel insights into the relationship between climate drivers and flood generation processes, and improving the ability to project future extreme events under changing climate.

Related Publication(s):

  1. Hwang, J., Schreck III, C. J., Aiyyer, A., Sankarasubramanian, A. (2024). Understanding the organizing scales of winter flood hydroclimatology and the associated drivers over the coterminous United States. Communications Earth & Environment, Under Review.
  2. Hwang, J., & Sankarasubramanian, A. (2024). Large-Scale Moisture Delivery Pathways Contributing to Winter Floods in the United States. Under Preparation (Preliminary results to be presented at AGU Fall Meeting 2024).

Enhancing water resources planning and facilitating climate adaptation

(Left) Return periods of (a) 1-day maximum precipitation, A* (b) 5-day precedent precipitation total, K5* (c) 30-day precedent precipitation total, K30* (d) joint occurrence of 1-day maximum precipitation and 5-day precedent precipitation total, (A*, K5*), and (e) joint occurrence of 1-day maximum precipitation and 30-day precedent precipitation total (A*, K30*) observed during dam failures at each location. / (Right) Spatial distribution of aging dams and recent linear trends in (a) joint event of 100-year annual maximum daily and 10-year previous 5-day rainfall, and (b) joint event of 100-year annual maximum daily and 10-year previous 30-day rainfall, for the 1979-2021 CPC-CONUS daily rainfall dataset. The color represents the ratio of the return level in 2021 to that in 1979.
  • Systematically analyzing the vulnerabilities of contemporary water resources systems and infrastructure under the changing climate conditions.
  • Developing a comprehensive forecasting system aimed at improving disaster management and resilience for at-risk populations, integrating remotely sensed data with machine learning and physical modeling.

Related Publication(s):

  1. Hwang, J., & Lall, U. (2024). Increasing dam failure risk in the USA due to compound rainfall clusters as climate changes. npj Natural Hazards, 1(1), 27.
  2. Hwang, J., Najibi, N., & Devineni, N. (2024). Quantifying the Spatial Coherence of Excess Rainfall across the Continental United States. Under Review.